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Grafana

AWS PrivateLink for Grafana Cloud

Navigating secure connectivity and managing data transfer costs are vital to optimizing your cloud-based services. AWS PrivateLink offers a solution to improve security and reduce costs, especially when integrating with Grafana Cloud. In this tutorial, we delve into the problems solved by PrivateLink, its working mechanism, and a step-by-step guide on configuring it to connect to Grafana services. What You'll Learn.

How continuous profiling improved code performance for a new Grafana Loki feature

Throughout the software development process, engineers can use a number of methods and tools to ensure their code is efficient. When using Go, for example, there are built-in tools, including those for benchmarking and CPU/memory profiling, to check how efficiently code will run. Engineers can also run unit tests to validate code quality.

Reducing data center carbon emissions with Hardware Sentry, Grafana, and OpenTelemetry

With just 30 employees, Sentry Software might be considered a small company, but they’re prioritizing sustainability in a big way. As the makers of Hardware Sentry, an IT monitoring software, a large part of their business relies on maintaining optimal temperature conditions at their data centers — an operation that contributes to the company’s overall carbon footprint.

How to deploy Grafana on Kubernetes (Grafana Office Hours #13)

Senior Developer Advocates Nicole van der Hoeven and Usman Ahmad talk about how to deploy Grafana on Kubernetes for beginners: what Kubernetes is, how it's an evolution of distributed computing, what its relationship to Docker is, other things you might need to know to work with Kubernetes, and how you can deploy Grafana on your own Kubernetes clusters.

Introducing Grafana OnCall shift swaps: A simpler way to exchange on-call shifts with teammates

A family member’s birthday, that concert you’ve waited all year to see, an impromptu weekend getaway with friends — there are a lot of reasons software engineers might want to switch on-call shifts. And rather than have to frantically send Slack messages to your teammates, wouldn’t it be nice to automate the process and quickly find the coverage you need?

Introducing the Prometheus Java client 1.0.0

PromCon, the annual Prometheus community conference, is around the corner, and this year I’ll have exciting news to share from the Prometheus Java community: The highly anticipated 1.0.0 version of the Prometheus Java client library is here! At Grafana Labs, we’re big proponents of Prometheus. And as a maintainer of the Prometheus Java client library, I highly appreciate the support, as it helps us to drive innovation in the Prometheus community.

OpenTelemetry metrics: A guide to Delta vs. Cumulative temporality trade-offs

In OpenTelemetry metrics, there are two temporalities, Delta and Cumulative and the OpenTelemetry community has a good guide on the different trade-offs of each. However, the guide tackles the problem from the SDK end. It does not cover the complexity that arises from the collection pipeline. This post takes that into account and covers the architecture and considerations that are involved end-to-end for picking the temporality.

How to monitor SLOs with Grafana, Grafana Loki, Prometheus, and Pyrra: Inside the Daimler Truck observability stack

In order for fleet managers at Daimler Truck to manage the day-to-day operations of their vast connected vehicles service, they use tb.lx, a digital product studio that delivers near real-time data along with valuable insights for their networks of trucks and buses around the world. Each connected vehicle utilizes the cTP, an installed piece of technology that generates a small mountain of telemetry data, including speed, GPS position, acceleration values, braking force and more.

Better anomaly detection in system observability and performance testing with Grafana k6

Grzegorz Piechnik is a performance engineer who runs his own blog, creates YouTube videos, and develops open source tools. He is also a k6 Champion. You can follow him here. From the beginning of my career in IT, I was taught to automate every repeatable aspect of my work. When it came to performance testing and system observability, there was always one thing that bothered me: the lack of automation. When I entered projects, I encountered either technological barriers or budgetary constraints.